I will discuss current research on the MapGraph platform. MapGraph is a new and disruptive technology for ultra-fast processing of large graphs on commodity many-core hardware. On a single GPU you can analyze the bitcoin transaction graph in .35 seconds. With MapGraph on 64 NVIDIA K20 GPUs, you can traverse a scale-free graph of 4.3 billion directed edges in .13 seconds for a throughput of 32 Billion Traversed Edges Per Second (32 GTEPS). I will explain why GPUs are an interesting option for data intensive applications, how we map graphs onto many-core processors, and what the future looks like for the MapGraph platform.
MapGraph provides a familiar vertex-centric abstraction, but its GPU acceleration is 100s of times faster than main memory CPU-only technologies and up to 100,000 times faster than graph technologies based on MapReduce or key-value stores such as HBase, Titan, and Accumulo. Learn more at http://MapGraph.io.
12. GPUs – A Game Changer for Graph Analytics
• Graphs are a hard problem
• Non-locality
• Data dependent parallelism
• Memory, PCIe bus and network are
bottlenecks
• Recent performance gains driven by
innovations in bottom-up search,
data layout, and partitioning.
• GPUs deliver effective parallelism
• 10x CPU FLOPS
• 10x CPU/RAM bandwidth
• Significant speeds up over CPU
• 3 GTEPS on one GPU
• 32 GTEPS on 64 GPU cluster
http://bigdata.com
http://mapgraph.io
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10x Speedup on GPUs